Time domain analysis of LLC resonant converters in the boost mode for battery charger applications
Bibliographic record
Abstract
In order to support different types of rechargeable batteries (e.g. Li-Ion, Lead-Acid, NiMh), the design of universal battery chargers must focus on wide output voltage regulation, rather than on constant voltage regulation. The universal battery charger is expected to provide a demanding output voltage range between nominal and 1.5 times nominal, while sustaining the maximum power delivery and maintaining high efficiency. The softly switched LLC resonant converter is one of the best topologies for designing battery chargers due to its ability to produce variable voltage gains in different operating frequencies, while providing soft switching for all semiconductor devices. The objective of this paper is to solve the Time Domain set equations of the LLC resonant converter in boost mode, and extract closed form answer for the voltage gain as a function of converter variables (e.g. input voltage, switching frequency, resonant elements, output load). The closed form answer can precisely predict the behavior of the LLC resonant converter and can be employed in order to design and optimize the LLC resonant converter in the boost mode. In this paper, the Time Domain (TD) analysis of the LLC resonant converter in the operating mode below the resonant frequency will be presented, and a closed form answer for the converter voltage gain will be extracted. The experimental results, extracted from a 1200W platform, shows that using the obtained voltage gain equation for the LLC resonant converter results in a far higher degree of accuracy than does using First Harmonic Approximation method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".